Embodiments of the present invention relate to rating samples, and more specifically, to a method and apparatus for generating a training model based on feedback in a rating system, and to a corresponding computer program product.
With the development of computer technology and artificial intelligence technology, it is possible to automatically and intelligently rate various data samples using a computer. An automatic rating system may learn standards which are adopted by experts (or users) to rate representative samples, generate training models by using these learned rating standards, and then rate other to-be-rated samples by using these training models.
Usually there is an enormous amount (e.g., hundreds of thousands or even millions) of to-be-rated samples. Hence, it is necessary to collect from users, scores of a considerable number of samples, so as to ensure the accuracy of the training model used in rating. However, for a specific user, it is time-consuming and laborious to rate thousands of samples, and the user is likely weary, such that errors might occur. Errors may include, for example, inconsistency between rating standards, misoperation and the like, which further cause “noise” during rating samples. These problems will affect the accuracy of the generated training model and further the accuracy of automatic rating.